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Group Sparsity Residual Constraint with Non-Local Priors for Image Restoration
Zhiyuan Zha1; Xin Yuan2; Bihan Wen3; Jiantao Zhou4; Ce Zhu1
2020-09-09
Source PublicationIEEE Transactions on Image Processing
ISSN1057-7149
Volume29Pages:8960-8975
Abstract

Group sparse representation (GSR) has made great strides in image restoration producing superior performance, realized through employing a powerful mechanism to integrate the local sparsity and nonlocal self-similarity of images. However, due to some form of degradation (e.g., noise, down-sampling or pixels missing), traditional GSR models may fail to faithfully estimate sparsity of each group in an image, thus resulting in a distorted reconstruction of the original image. This motivates us to design a simple yet effective model that aims to address the above mentioned problem. Specifically, we propose group sparsity residual constraint with nonlocal priors (GSRC-NLP) for image restoration. Through introducing the group sparsity residual constraint, the problem of image restoration is further defined and simplified through attempts at reducing the group sparsity residual. Towards this end, we first obtain a good estimation of the group sparse coefficient of each original image group by exploiting the image nonlocal self-similarity (NSS) prior along with self-supervised learning scheme, and then the group sparse coefficient of the corresponding degraded image group is enforced to approximate the estimation. To make the proposed scheme tractable and robust, two algorithms, i.e., iterative shrinkage/thresholding (IST) and alternating direction method of multipliers (ADMM), are employed to solve the proposed optimization problems for different image restoration tasks. Experimental results on image denoising, image inpainting and image compressive sensing (CS) recovery, demonstrate that the proposed GSRC-NLP based image restoration algorithm is comparable to state-of-the-art denoising methods and outperforms several testing image inpainting and image CS recovery methods in terms of both objective and perceptual quality metrics.

KeywordGroup Sparse Representation Group Sparsity Residual Constraint Image Restoration Nonlocal Self-similarity
DOI10.1109/TIP.2020.3021291
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000572623700003
Scopus ID2-s2.0-85092197562
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Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorCe Zhu
Affiliation1.School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu,China
2.Nokia Bell Labs, Murray Hill, NJ 07974 USA
3.School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798
4.State Key Laboratory of Internet of Things for Smart City, Department of Computer and Information Science, University of Macau, Taipa 999078, Macau
Recommended Citation
GB/T 7714
Zhiyuan Zha,Xin Yuan,Bihan Wen,et al. Group Sparsity Residual Constraint with Non-Local Priors for Image Restoration[J]. IEEE Transactions on Image Processing,2020,29:8960-8975.
APA Zhiyuan Zha,Xin Yuan,Bihan Wen,Jiantao Zhou,&Ce Zhu.(2020).Group Sparsity Residual Constraint with Non-Local Priors for Image Restoration.IEEE Transactions on Image Processing,29,8960-8975.
MLA Zhiyuan Zha,et al."Group Sparsity Residual Constraint with Non-Local Priors for Image Restoration".IEEE Transactions on Image Processing 29(2020):8960-8975.
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